On-line enhanced control method for improving lateral stability of symmetric high-speed aircraft

Abstract

The application discloses an online enhancement control method for improving lateral stability of a face-symmetrical high-speed aircraft, which comprises the following steps: S1, designing a baseline controller; S2, designing an online identification algorithm of lateral coupling dynamic coefficients; and S3, designing an identification coordination mechanism based on adaptive gain adjustment. The application can solve the problem of small adaptability range of the face-symmetrical high-speed aircraft lateral control to uncertainty, and on the basis of a nonlinear dynamic inverse simplified control model, through designing a mutual coordination mechanism of "parameter identification based on double linear transformation + non-overlapping sliding window" and "adaptive gain adjustment", when the uncertainty is small, the performance of the baseline controller is not affected, and when the uncertainty exceeds the lateral control margin, through a limited number of parameter identifications, the aircraft can be recovered from the limit flight state, the control quality of the controller under strong coupling, strong uncertainty and low margin is effectively improved, and support is provided for autonomous adaptive flight of the face-symmetrical high-speed aircraft in the whole airspace.

Description

Technical Field

[0001] This invention relates to the field of high-speed aircraft flight control, specifically to an online enhanced control method for improving the lateral stability of symmetrical high-speed aircraft. Its key feature is that it addresses the problems of low lateral control stability margin and weak adaptability to uncertainties in symmetrical high-speed aircraft, enabling the aircraft to recover control from extreme flight states and effectively improving the control quality of the controller under conditions of strong coupling, strong uncertainty, and low margin. Background Technology

[0002] High-speed symmetrical aircraft possess advantages such as long flight distance, rapid arrival, and wide coverage. While pursuing optimal flight performance in the lifting surface direction, they often introduce strong coupling interference in the lateral direction, including aerodynamic and dynamic coupling. This degrades the lateral control response performance and reduces the stability margin of traditional PID controllers. For example, typical PID controller design requirements dictate a system amplitude margin exceeding ±6dB and a phase margin exceeding 30° to accommodate uncertainties during flight. However, as the flight airspace expands and the flight velocity range changes rapidly, the lateral coupling magnitude increases and parameter variations become drastic. Without sacrificing the aircraft's rapid response, the system's lateral stability margin is often designed to be even lower. When significant uncertainties arise during flight, the system will approach or exceed the controller's performance limits, leading to a decline in flight control quality and mission failure.

[0003] Currently, the uncertainty adaptation range of flight control is mainly improved through interference observation compensation technology or adaptive control technology. When using interference observation compensation technology, the high-gain design of the observer is not conducive to suppressing complex vibration environments for symmetrical high-speed aircraft, and may reduce the original stability margin of the controller. When using adaptive control technology, it is difficult to adapt to rapid and drastic changes in parameters or uncertainties. Moreover, as the number of control parameters increases, its adaptive law makes the system nonlinearity more complex, and it is difficult to guarantee stability across the entire airspace, posing a significant challenge in engineering applications. Therefore, how to design a simple controller suitable for engineering applications, enabling the aircraft to adapt to large uncertainties under low stability margins, and still recover control even when uncertainties exceed the stability margin range, is an important problem that future flight control needs to solve. Summary of the Invention

[0004] The purpose of this invention is to provide an online enhanced control method to improve the lateral stability of symmetrical high-speed aircraft. Based on a nonlinear dynamic inverse simplified control model, a coordinated mechanism of "parameter identification based on bilinear transformation + non-overlapping sliding window" and "adaptive gain adjustment" is designed. When the uncertainty is small, it does not affect the performance of the baseline controller. When the uncertainty exceeds the lateral control margin, the aircraft can recover control from the extreme flight state through only a finite number of parameter identifications. This effectively improves the control quality of the controller under strong coupling, strong uncertainty, and low margin, and provides support for the autonomous adaptive flight of symmetrical high-speed aircraft in the entire airspace.

[0005] To achieve the above objectives, the present invention provides an online enhanced control method for improving the lateral stability of a surface-symmetrical high-speed aircraft, characterized by comprising the following steps:

[0006] S1. Design of Baseline Controller: Based on the lateral coupling dynamic model of the aircraft, a decoupling control law is designed using the nonlinear dynamic inverse method, the object model is transformed into an integral system, and a baseline controller based on sideslip angle and roll angle feedback is designed using the PID control method.

[0007] S2. Design an online identification algorithm for lateral coupling dynamic coefficients: To address the issue that the baseline controller requires high-precision coupling dynamic coefficients, the lateral coupling dynamic model is discretized through bilinear transformation. A non-overlapping sliding window strategy is used to sample flight status information and rudder deflection command information online, and a recursive least squares algorithm is used to identify the coupling dynamic coefficients.

[0008] S3. Design an identification coordination mechanism based on adaptive gain adjustment: To address the problem of identification algorithm failure when control approaches divergence, an adaptive gain adjustment algorithm is designed. This algorithm comprehensively utilizes lateral attitude tracking error and pseudo-control command power density to adjust the total control gain online. The timing of the identification algorithm's operation is determined by the change in total gain, forming a coordination mechanism between online adjustment of total gain and coefficient identification. This reduces the number of identification attempts, improves identification accuracy, and enables the system to recover control from the divergent state.

[0009] Furthermore, the lateral coupling dynamics model of the aircraft in step S1 is as follows:

[0010] ,

[0011] In the formula, Sideslip angle, The tilt angle, For the angle of attack, For velocity tilt angle, aircraft speed, and It can be obtained through navigation calculation; The rolling angular velocity, Yaw angular velocity, The pitch angular velocity, For rolling rudder deflection, For yaw rudder deflection, and , , It can be measured by sensors on the aircraft. For the spacecraft's body coordinate system Moment of inertia of the shaft, For the spacecraft's body coordinate system Moment of inertia of the shaft, For the spacecraft's body coordinate system Moment of inertia of the shaft, Let be the lateral inertial product, and be the known overall parameters of the aircraft. Let be the coupling dynamic coefficients to be identified; and have ,

[0012] In the formula, These are the rolling moment coefficients generated by sideslip angle, roll deflection, and yaw deflection, respectively. These are the yaw moment coefficients generated by sideslip angle, yaw rudder deflection, and roll rudder deflection, respectively.

[0013] Based on the lateral coupling dynamic model, the decoupling control law of the nonlinear dynamic inverse is obtained as follows: ,

[0014] In the formula, , for the aircraft's actuator commands, , which is the pseudo-control variable of the aircraft. The 2×1 dimensional state vector of the aircraft is represented as follows: ,

[0015] The 2×2 dimensional aircraft control matrix is ​​represented as follows:

[0016] ,

[0017] Furthermore, in order to obtain the pseudo-control variables of the aircraft Based on sideslip angle yaw rate Tilt angle Roll angular velocity Design a PID controller with feedback and make it track the desired sideslip angle. Tilt angle The PID controller is as follows: ,

[0018] In the formula, This is the total gain of the controller, used for adaptive gain adjustment. These are PID control parameters;

[0019] .

[0020] Furthermore, S2 designs an online identification algorithm for the lateral coupling dynamic coefficients; firstly, the lateral coupling dynamic model of the aircraft is discretized into the form of the model to be identified through bilinear transformation:

[0021] ,

[0022] In the formula, Indicates the first The value of the shot, Let be an 8-dimensional row vector representing the parameters to be identified, with the following specific form:

[0023] ,

[0024] This is a 3×8 matrix used to store flight state variables and rudder deflection information, represented as follows:

[0025] ,

[0026] The difference between state variables is expressed as follows:

[0027] ,

[0028] The dynamic coupling equations are expressed as follows:

[0029] ,

[0030] Then sample it. shoot( Historical status information and rudder deflection command information data, i.e. The model to be identified is extended to the following form: ,

[0031] Finally, throughout the entire flight of the aircraft, a non-overlapping sliding window strategy was used to sample flight status information and rudder deflection command information online, and a recursive least squares algorithm was used to solve for the identification parameters. The non-overlapping sliding window strategy refers to the strategy used by the identification algorithm in the discrete time intervals during its operation. within, will 3D sampling space exist According to The time interval between shots shifts.

[0032] Furthermore, S3. Design an identification and coordination mechanism based on adaptive gain adjustment; the designed adaptive gain adjustment algorithm is as follows:

[0033] ,

[0034] In the formula, These represent the total gain of the controller, with an initial value of 1. This represents the minimum total gain. This is the maximum total gain, i.e.

[0035] These are the design parameters for the adaptive gain adjustment algorithm. The design needs to be based on low-frequency attitude tracking errors. and the power density of pseudo-control commands with high divergence frequency A trade-off is made to achieve an increase in overall gain and a decrease in the rate of increase. The design aims to ensure that the total gain converges to 1 at a rate of change when the attitude tracking error and pseudo-control command power density are small; attitude tracking error Output for reference model Compared with the actual aircraft status The difference, that is

[0036] The corresponding reference model is:

[0037] The power density calculation method for pseudo-control commands is as follows:

[0038] In the formula, the transfer function For a high-pass filter, its parameters The selection should ensure that the cutoff frequency is before the divergence frequency of the controller in the high-frequency range, and the transfer function... For a low-pass filter, its parameters The selection of the cutoff frequency should ensure that it is lower than the cutoff frequency of the high-pass filter; the remaining variables are intermediate variables.

[0039] When the total gain The identification algorithm is activated only when the system is in operation, and is not activated at other times. This forms a coordination mechanism between online adjustment of total gain and coefficient identification, reducing the number of identification attempts, improving identification accuracy, and restoring the system from a divergent state to control.

[0040] This invention presents an online enhanced control method for improving the lateral stability of symmetrical high-speed aircraft, which has the following advantages: It employs a baseline controller design method of "PID + nonlinear dynamic inverse," which decouples the system while reducing the difficulty of analyzing and engineering applications of the controller's basic performance; it uses a parameter identification algorithm based on "bilinear transformation + non-overlapping sliding window" to identify coupling dynamic coefficients, ensuring the online update capability of the controller's stability domain under uncertainty and low-margin design; and it employs an identification coordination mechanism based on "adaptive gain adjustment," which improves stability when uncertainty exceeds the stability margin, creating conditions for reducing the online update of the controller's stability domain, while ensuring that the algorithm does not affect the performance of the baseline controller when uncertainty is small. This method, without changing the baseline controller structure, enables the aircraft to recover control from its extreme flight state through only a limited number of parameter identifications, effectively improving the controller's control quality under strong coupling, strong uncertainty, and low margin conditions, and providing support for the autonomous adaptive flight of symmetrical high-speed aircraft across the entire airspace. Attached Figure Description

[0041] Figure 1 This is a flowchart of an online enhanced control method for improving the lateral stability of a surface-symmetrical high-speed aircraft, according to the present invention.

[0042] Figure 2 This is a schematic diagram of a non-overlapping sliding window strategy in a specific embodiment of the present invention;

[0043] Figure 3 This is a comparison of yaw channel control results in specific embodiments of the present invention;

[0044] Figure 4 This is a comparison of the rolling channel control results in a specific embodiment of the present invention;

[0045] Figure 5 This describes the adaptive change process of total gain in a specific embodiment of the present invention;

[0046] Figure 6 This refers to the accuracy of identifying the lateral coupling dynamic coefficient in a specific embodiment of the present invention. Detailed Implementation

[0047] The present invention will be further described below with reference to the accompanying drawings and by providing a detailed description of a preferred embodiment.

[0048] like Figure 1 As shown, an online enhanced control method for improving the lateral stability of a surface-symmetrical high-speed aircraft includes the following steps:

[0049] S1. Design the baseline controller;

[0050] Based on the lateral coupling dynamics model of the aircraft, a decoupling control law is designed using a nonlinear dynamic inverse method, transforming the object model into an integral system, and a baseline controller based on sideslip angle and roll angle feedback is designed using a PID control method.

[0051] The lateral coupling dynamics model of the aircraft is as follows:

[0052] ,

[0053] In the formula, Sideslip angle, The tilt angle, For the angle of attack, For velocity tilt angle, aircraft speed, and It can be obtained through navigation calculation; The rolling angular velocity, Yaw angular velocity, The pitch angular velocity, For rolling rudder deflection, For yaw rudder deflection, and , , It can be measured by sensors on the aircraft. For the spacecraft's body coordinate system Moment of inertia of the shaft, For the spacecraft's body coordinate system Moment of inertia of the shaft, For the spacecraft's body coordinate system Moment of inertia of the shaft, Let be the lateral inertial product, and be the known overall parameters of the aircraft. Let be the coupling dynamic coefficients to be identified; and have ,

[0054] In the formula, These are the rolling moment coefficients generated by sideslip angle, roll deflection, and yaw deflection, respectively. These are the yaw moment coefficients generated by sideslip angle, yaw rudder deflection, and roll rudder deflection, respectively.

[0055] Based on the lateral coupling dynamic model, the decoupling control law of the nonlinear dynamic inverse is obtained as follows: ,

[0056] In the formula, , for the aircraft's actuator commands, , which is the pseudo-control variable of the aircraft. The 2×1 dimensional state vector of the aircraft is represented as follows: ,

[0057] The 2×2 dimensional aircraft control matrix is ​​represented as follows:

[0058] .

[0059] Furthermore, in order to obtain the pseudo-control variables of the aircraft Based on sideslip angle yaw rate Tilt angle Roll angular velocity Design a PID controller with feedback and make it track the desired sideslip angle. Tilt angle The PID controller is as follows: ,

[0060] In the formula, This is the total gain of the controller, used for adaptive gain adjustment. These are PID control parameters;

[0061] .

[0062] S2 designs an online identification algorithm for lateral coupling dynamic coefficients;

[0063] To address the issue of the baseline controller requiring high-precision coupled dynamic coefficients, the lateral coupled dynamic model is discretized through bilinear transformation. A non-overlapping sliding window strategy is used to sample flight status information and rudder deflection command information online, and a recursive least squares algorithm is employed to identify the coupled dynamic coefficients.

[0064] First, the lateral coupled dynamics model of the aircraft is discretized into the form of the model to be identified using a bilinear transformation:

[0065] ,

[0066] In the formula, Indicates the first The value of the shot, Let be an 8-dimensional row vector representing the parameters to be identified, with the following specific form:

[0067] ,

[0068] This is a 3×8 matrix used to store flight state variables and rudder deflection information, represented as follows:

[0069] ,

[0070] The difference between state variables is expressed as follows:

[0071] ,

[0072] The dynamic coupling equations are expressed as follows:

[0073] ,

[0074] Then sample it. shoot( Historical status information and rudder deflection command information data, i.e. The model to be identified is extended to the following form: ,

[0075] Finally, throughout the entire flight of the aircraft, a non-overlapping sliding window strategy was used to sample flight status information and rudder deflection command information online, and a recursive least squares algorithm was used to solve for the identification parameters. The non-overlapping sliding window strategy refers to the strategy used by the identification algorithm in the discrete time intervals during its operation. within, will 3D sampling space exist According to The time interval between shots shifts.

[0076] S3. Design an identification and coordination mechanism based on adaptive gain adjustment;

[0077] To address the issue of poor identification accuracy in identification algorithms when control approaches divergence, an adaptive gain adjustment algorithm is designed. This algorithm comprehensively utilizes lateral attitude tracking error and pseudo-control command power density to adjust the total control gain online. The timing of the identification algorithm's operation is determined by the change in the total gain, forming a coordination mechanism between online adjustment of the total gain and coefficient identification. This reduces the number of identification attempts, improves identification accuracy, and simultaneously enables the system to recover control from a divergent state.

[0078] The designed adaptive gain adjustment algorithm is as follows:

[0079] ,

[0080] In the formula, These represent the total gain of the controller, with an initial value of 1. This represents the minimum total gain. This is the maximum total gain, i.e.

[0081] These are the design parameters for the adaptive gain adjustment algorithm. The design needs to be based on low-frequency attitude tracking errors. and the power density of pseudo-control commands with high divergence frequency A trade-off is made to achieve an increase in overall gain and a decrease in the rate of increase. The design aims to ensure that the total gain converges to 1 at a rate of change when the attitude tracking error and pseudo-control command power density are small; attitude tracking error Output for reference model Compared with the actual aircraft status The difference, that is

[0082] The corresponding reference model is:

[0083] The power density calculation method for pseudo-control commands is as follows:

[0084] In the formula, the transfer function For a high-pass filter, its parameters The selection should ensure that the cutoff frequency is before the divergence frequency of the controller in the high-frequency range, and the transfer function... For a low-pass filter, its parameters The selection of the cutoff frequency should ensure that it is lower than the cutoff frequency of the high-pass filter; the remaining variables are intermediate variables.

[0085] When the total gain The identification algorithm is activated only when the system is in operation, and is not activated at other times. This forms a coordination mechanism between online adjustment of total gain and coefficient identification, reducing the number of identification attempts, improving identification accuracy, and restoring the system from a divergent state to control.

[0086] Based on the detailed steps described above, online enhanced control of the lateral stability of a symmetrical high-speed aircraft can be achieved.

[0087] Specific applications:

[0088] Based on the controller design described above, the controlled object is a complete lateral dynamic model of the aircraft, and a step response simulation is performed. The aerodynamic force and aerodynamic moment coefficients in the model are obtained through aerodynamic interpolation tables. The lateral dynamic coefficients involved in the controller are only initialized as follows:

[0089] Table 1 Initial values ​​of lateral dynamic coefficients

[0090] b2 b3 b4 b5 b6 c2 c3 c4 -72.13 20.59 -0.1001 -0.0072 -4.42 2007.2 926.3 260.2

[0091] The initial flight status and commands are as follows:

[0092] Table 2 Initial values ​​of aircraft commands

[0093] Initial value of sideslip angle (°) Initial value of tilt angle (°) Sideslip angle command (°) Tilt angle command (°) 0 0 0 30

[0094] Within a 12-second simulation period, a 0.7x torque was applied at the 4th second, and no torque was applied at the 8th second, i.e., a ±30% torque was applied to verify the effectiveness of the method.

[0095] Simulation results are as follows Figures 3-6 As shown, when using only the dynamic inverse + PID control method, the controller diverges under ±30% torque pull. However, when using the control method designed in this invention, the controller quickly recovers from the divergent state and stabilizes. This indicates that the online enhanced control method designed in this invention can significantly improve the controller's adaptability to uncertainty. Even under divergent conditions, the adaptive gain adjustment algorithm improves the stability of identification. Identification is activated when the gain changes (4~6s and 8~10s). After identification, the controller's stable domain is shifted, with an identification error not exceeding 10%. The control gain recovers rapidly after the system stabilizes, ensuring the controller remains within an optimal range in subsequent operations. This demonstrates that the method is reasonable and effective, significantly improving control performance.

[0096] Although the present invention has been described in detail through the preferred embodiments above, it should be understood that the above description should not be considered as a limitation of the present invention. Various modifications and substitutions to the present invention will be apparent to those skilled in the art after reading the above description. Therefore, the scope of protection of the present invention should be defined by the appended claims.

[0097] The parts of this invention not described in detail are common knowledge to those skilled in the art.

Claims

1. An online enhanced control method for improving the lateral stability of a surface-symmetrical high-speed aircraft, characterized in that, Includes the following steps: S1. Design of Baseline Controller: Based on the lateral coupling dynamic model of the aircraft, a decoupling control law is designed using the nonlinear dynamic inverse method, the object model is transformed into an integral system, and a baseline controller based on sideslip angle and roll angle feedback is designed using the PID control method. S2. Design an online identification algorithm for lateral coupling dynamic coefficients: To address the issue that the baseline controller needs to use high-precision coupling dynamic coefficients, the lateral coupling dynamic model is discretized through bilinear transformation. A non-overlapping sliding window strategy is used to sample flight status information and rudder deflection command information online, and a recursive least squares algorithm is used to identify the coupling dynamic coefficients. S3. Design an identification coordination mechanism based on adaptive gain adjustment: To address the problem of identification algorithm failure when control approaches divergence, an adaptive gain adjustment algorithm is designed. This algorithm comprehensively utilizes lateral attitude tracking error and pseudo-control command power density to adjust the total control gain online. The timing of the identification algorithm's operation is determined by the change in total gain, forming a coordination mechanism between online adjustment of total gain and coefficient identification. This reduces the number of identification attempts, improves identification accuracy, and enables the system to recover control from the divergent state.

2. The online enhanced control method for improving the lateral stability of a surface-symmetrical high-speed aircraft as described in claim 1, characterized in that, The lateral coupling dynamics model of the aircraft in step S1 is as follows: ， In the formula, Sideslip angle, The tilt angle, For the angle of attack, For velocity tilt angle, aircraft speed, and It can be obtained through navigation calculation; The rolling angular velocity, Yaw angular velocity, The pitch angular velocity, For rolling rudder deflection, For yaw rudder deflection, and , , It can be measured by sensors on the aircraft. For the spacecraft's body coordinate system Moment of inertia of the shaft, For the spacecraft's body coordinate system Moment of inertia of the shaft, For the spacecraft's body coordinate system Moment of inertia of the shaft, Let be the lateral inertial product, and be the known overall parameters of the aircraft. Let be the coupling dynamic coefficients to be identified; and have , In the formula, These are the rolling moment coefficients generated by sideslip angle, roll deflection, and yaw deflection, respectively. These are the yaw moment coefficients generated by sideslip angle, yaw rudder deflection, and roll rudder deflection, respectively. Based on the lateral coupling dynamic model, the decoupling control law of the nonlinear dynamic inverse is obtained as follows: , In the formula, , for the aircraft's actuator commands, , which is the pseudo-control variable of the aircraft. The 2×1 dimensional state vector of the aircraft is represented as follows: , The 2×2 dimensional aircraft control matrix is ​​represented as follows: ， In order to obtain the pseudo-control variables of the aircraft Based on sideslip angle yaw rate Tilt angle Roll angular velocity Design a PID controller with feedback and make it track the desired sideslip angle. Tilt angle The PID controller is as follows: , In the formula, This is the total gain of the controller, used for adaptive gain adjustment. These are PID control parameters; 3. The online enhanced control method for improving the lateral stability of a surface-symmetrical high-speed aircraft as described in claim 2, characterized in that, First, the lateral coupled dynamics model of the aircraft is discretized into the form of the model to be identified using a bilinear transformation: , In the formula, Indicates the first The value of the shot, Let be an 8-dimensional row vector representing the parameters to be identified, with the following specific form: ， This is a 3×8 matrix used to store flight state variables and rudder deflection information, represented as follows: ， The difference between state variables is expressed as follows: ， The dynamic coupling equations are expressed as follows: ， Then sample it. shoot( Historical status information and rudder deflection command information data, i.e. The model to be identified is extended to the following form: , Finally, throughout the entire flight of the aircraft, a non-overlapping sliding window strategy was used to sample flight status information and rudder deflection command information online, and a recursive least squares algorithm was used to solve for the identification parameters. The non-overlapping sliding window strategy refers to the strategy used by the identification algorithm in the discrete time intervals during its operation. within, will 3D sampling space exist According to The time interval between shots shifts.

4. The online enhanced control method for improving the lateral stability of a surface-symmetrical high-speed aircraft as described in claim 3, characterized in that, By comprehensively utilizing lateral attitude tracking error and pseudo-control command power density, the overall control gain is adjusted online; the designed adaptive gain adjustment algorithm is as follows: , In the formula, These represent the total gain of the controller, with an initial value of 1. This represents the minimum total gain. This is the maximum total gain, i.e. These are the design parameters for the adaptive gain adjustment algorithm. The design needs to be based on low-frequency attitude tracking errors. and the power density of pseudo-control commands with high divergence frequency A trade-off is made to achieve an increase in overall gain and a decrease in the rate of increase. The design aims to ensure that the total gain converges to 1 at a rate of change when the attitude tracking error and pseudo-control command power density are small; attitude tracking error Output for reference model Compared with the actual aircraft status The difference, that is The corresponding reference model is: The power density calculation method for pseudo-control commands is as follows: In the formula, the transfer function For a high-pass filter, its parameters The selection should ensure that the cutoff frequency is before the divergence frequency of the controller in the high-frequency range, and the transfer function... For a low-pass filter, its parameters The selection of the cutoff frequency should ensure that it is lower than the cutoff frequency of the high-pass filter; the remaining variables are intermediate variables.

5. The online enhanced control method for improving the lateral stability of a surface-symmetrical high-speed aircraft as described in claim 4, when the total gain... The identification algorithm is activated only when the system is in operation, and is not activated at other times. This forms a coordination mechanism between online adjustment of total gain and coefficient identification, reducing the number of identification attempts, improving identification accuracy, and restoring the system from a divergent state to control.

Images